7th East African Health & Scientific Conference 27th - 29th March, 2019 Dar Es Salaam, Tanzania Whole genome sequence-based characterisation of clinical Escherichia coli isolates Tolbert Sonda, t.sonda@kcri.ac.tz Data Management/Bioinformatics Unit

Background E. coli is an important causative agent for Diarrhoea Neonatal meningitis Hemolytic uremic syndrome outbreaks (China, German) UTI 64.5% in Kenya, up to 79% in Tanzania ESBL global estimates by 2030 in E. coli will be 77.0% Critical priority, AMR in E. coli a threat to economy, health (WHO, 2018)

Background Economic loss Lots of health-care resources e.g. 500mg Ampicillin for Sh500 (0.2 USD) vs. 1g Meropenem for Sh25,250 (11.5 USD) 2.0 - 3.5% reduction of a country’s GDP equivalent to USD 100 trillion of global cost Loss of life Increased risk of treatment failure and death 10 million deaths/year of AMR associated infections Psychosocial effects Sufferings due to prolonged hospitalisation and medication

Gap in diagnostic tools, data High AMR but scarce data in Africa Only 23 (42.6 %) African countries had AMR data (Tadesse et al., 2017) Lack of key tools to tackle AMR in Africa Laboratory capacity, surveillance, IPC Antibiotic name E. coli Penicillins Median (IQR) Amoxicillin 88.1 (81.4–92.6) Ampicillin 86.7 (69.2–96.7) Amoxi-clavu 43.5 (30.8–61.9) Cephalosporins Ceftriaxone 31.5 (6.9–47.7) Cefotaxime 26.8 (8.3–64.5) Carbapenems Meropenem 0 (0–5.7) Imipenem 0.2 (0–5.5)

A need for robust tools For diagnostics Inform clinical therapy/treatment within a short TAT Early detection of source strains in hospital outbreaks For surveillance investigations Impact assessment of AMR stewardships or interventions Data creation for epidemic preparedness/response NextGen Sequencing and bioinformatics present potentials for microbial investigations and diagnostics Next Gen sequencing/Bioinformatics holds the current and future potentials for microbial investigations and diagnostics

Objectives To establish NGS infrastructure for microbial investigations and diagnostics To determine sequence types, virulence genes in E. coli To identify resistance genes encoding ESBL (and others) in E. coli To determine the molecular relatedness of E. coli using phylogenetic trees

Design, recruitment, data Wards Clinics Identification Inclusion KCRI Data archive Micro unit Data unit Analysis Data Genomics unit Bioinformatics unit Data storage Sharing Global Repositories Biotech. repository

Bioinformatics STUFF Assembly (de novo) MiSeq MAGIC ↓ images Classify call bases demultiplex trim … Generate FASTQ READS Assembly (de novo) Mapping (on reference) Classify (typing) Species typing Variant finding STUFF DATA MiSeq Phylogeny construction Phenotype prediction K-merise (multi-purpose) Functional annotation MLST Identification Drug resistance Virulence SNP/Indels Diagnostics Transmission control Outbreak investigation Comparative genomics ANALYSIS Prediction

Genomics/Bioinfo infrastracture https://kcri.it:8043/#bioinformatics

Findings, discussion ST-131 2007 MDR outbreak in USA, Johnson et al. ESBL=73.7%% ST-131 2007 MDR outbreak in USA, Johnson et al. Sonda et al., 2018

Findings, discussion ST-10 complex (ST617) 1991 UTI outbreak in DKK CTX-M predominance Sonda et al., 2018

Findings, discussion High clonal diversity of E. coli 21 different STs circulating O25:H4-ST131 and O89:H10-ST617 predominant ESBL genes >70%, O25:H4-ST131, major cause of MDR E. coli infections Multiple virulence found iha in 22 (57.9%) for host cells adherence iss in 25 (65.7% for immune evasion Multiple virulence underlines the exceptional ability of E. coli

Conclusion, Implications NGS is an effective early warning system/tool: Resistance, virulence and emerging pathogens Outbreak source tracing Hospital surveillance (Commensal) E. coli are an important arsenal to dealt with Heavily loaded with AMR and virulence genes NGS/bioinformatics are promising tools

Stop AMR Simple practices can stop AMR spread Visit hospital premises only when needed Hygiene eg hands, clinical coats Use antimicrobials only when needed Finish antimicrobial treatment courses

Supervision, acknowledgements Kilimanjaro Clinical Research Institute Prof Blandina Mmbaga East Africa Health Research Commission Prof Gibson Kibiki Technical Univ. of Denmark Prof Frank Aarestrup Prof Ole Lund University of Copenhagen Prof Michael Alifrangis